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The purpose of this study is to propose a classification method for building demolition waste (DW) that is different from existing studies and to develop a demolition waste generation rate (DWGR) prediction model. To achieve the purpose, the chi-squared automatic interaction detection (CHAID), which is a decision tree (DT) method, was used in this study. Additionally, 796 buildings were measured using the data collection method that calculated the quantity of waste generation through actual measurements immediately before the building removal. The results using CHAID allows us to easily understand the complex influencing relationships between the DW types and various factors influencing the DW generation. Furthermore, the CHAID method was developed for forecasting the DWGR. Then, split-sample validation was performed to confirm the prediction performance of the CHAID algorithm applied in this study. The results show that the prediction performance of the current study is higher than that of the previous studies. In particular, the CHAID model for concrete classifies approximately 98.9% of the concrete generation correctly. Because the CHAID model of this study was developed from previous building cases, it can assist construction companies and building demolition contractors in decision making. [ABSTRACT FROM AUTHOR]

Information on waste generation rate (WGR) is useful for waste management. Recently, several studies have been conducted to predict WGR using artificial intelligence (AI) to the end of realizing smart waste management. Additionally, to improve the performance of machine learning (ML) predictive models, several strategies have also been tested of recent. This study aimed to develop a hybrid ML predictive model to enhance prediction performance for small datasets consisting mainly of categorical variables. Artificial neural network (multi-layer perceptron) (ANN (MLP)) and support vector machine regression (SVMR) algorithms were selected, and categorical principal components analysis (CATPCA) was applied. Accordingly, four predictive models—ANN (MLP), SVMR, CATPCA–ANN (MLP), and CATPCA–SVMR—were developed. The CATPCA–ANN (MLP) model showed some improvements in statistical metrics as compared to the ANN (MLP) model, and the CATPCA–SVMR model showed a far better performance across all statistical metrics than the SVMR model. The best prediction performance was found in the CATPCA–SVMR model (R 2 = 0.594, R = 0.770), which was thus considered the best model of the four developed. Here, the mean DWGR was 1165.04 kg/m2 for the observed values, and that for the predicted values was 1161.52 kg/m2. Thus, a novel method was proposed for developing a hybrid ML model to enhance prediction performance for small datasets consisting of categorical variables. The results of this study enable the use of ML algorithms, which are disadvantageous with respect to use of categorical variables, by using CATPCA, and we suggest a new AI approach to develop a predictive DWGR model with excellent predictive performance. • ANN (MLP) and SVMR algorithms were applied to datasets comprising categorical variables. • CATPCA was performed to convert categorical variables into continuous variables. • ANN (MLP), SVMR, CATPCA–ANN (MLP) and CATPCA–SVMR were compared. • Prediction performance was enhanced for CATPCA–ANN (MLP) and CATPCA–SVMR. • CATPCA–SVMR hybrid ML model is proposed as the best model for small DWG datasets. [ABSTRACT FROM AUTHOR]

When buildings that contain asbestos collapse, harmful asbestos fibers can spread in large quantities. It is critical to control this scattering as quickly as possible to protect residents and workers. Rescue and recovery operations in post-disaster situations usually take a lot of time. Therefore, experiments were conducted to evaluate and control the scattering of asbestos fibers. First, the concentration of asbestos fibers released from broken pieces of asbestos cement roofing sheet (ACRS) was measured over time. Next, the broken pieces were classified into three groups (A: no coating, B: water coating; C: asbestos stabilizer coating) and the amount of asbestos fiber released over time was measured to determine the efficiency of asbestos fiber control using water and asbestos stabilizer. The concentration of asbestos fibers generated from broken ACRS was 0.162 f/cc on average. The asbestos fiber control efficiencies of water and asbestos stabilizer showed similar patterns in the early stage. However, the efficiency of the water coating rapidly decreased after 9 h, whereas the asbestos stabilizer coating showed high efficiency even after 36 h. The results of this study will contribute to the establishment of plans for protecting workers and survivors more safely in disaster sites while controlling asbestos fibers. [ABSTRACT FROM AUTHOR]

This study investigates the recycling potential of demolition waste (DW) according to building structure, while considering environmental and economic aspects For that, this study surveyed 1,034 residential buildings in Korea immediately before demolition to collect reliable information on demolition waste generation rates (DWGRs). This study classified the removal stages of buildings into the demolition, collection and sorting, transportation, and disposal stages. This study suggested a method for carbon emissions calculation for each stage and carried out an inventory analysis. The economic value of recycled DW materials was also calculated. Furthermore, the recycling potential was calculated based on the economic value and the environmental load for the current scenario, i.e., the current waste recycling rate in Korea, and the maximum scenario, i.e., the maximum theoretical recycling rate. Regarding building structures, the recycling potential of wooden structures was the highest in both the scenarios. However, masonry-block structures showed improved recycling potential in the maximum scenario. Regarding DW types, the recycling potential of plastics was the highest, with plastics from reinforced concrete (RC) structures showing 6.6 times higher recycling potential than those from wooden structures. And the possibility of improving the recycling potential was higher for glass and plastics than aggregates, timber, and metals. Through the above research, this paper devised an approach that can be used to plan a detailed construction and demolition waste management strategy, considering building structures and DW types, and this method can also be applied to other regions and countries. • Reliable DW data were collected through field surveys of 736 residential buildings. • Even the same DW type, the CO2 emission differs depends on building structure type. • Recycling potential (RP) was calculated considering building structures and DW types. • Metals and masonry-block structure showed the highest RP in current situation. • RP of glass and wood structure is most likely to increase in Korea situation. [ABSTRACT FROM AUTHOR]

Accurately predicting different types and quantities of wastes is crucial for proper waste management and sustainable growth and development. High levels of disaster waste (DW) can negatively impact emergency responses, public health, and recovery and rebuilding processes, making the effective management of DW highly important. Most existing waste identification methods cannot suitably identify DW since it is highly subjective in form. In this study, we examined the applicability of different deep learning-based image recognition techniques for DW recognition. A DW image dataset was collected and categorized into normal and hard cases. Then, experiments were conducted using these three network models: DeepLabV3+, ResNeSt-50, and ResNeSt-101. The experimental results indicated that the mean intersection over union (mIoU) of the DeepLabV3+ model was 0.802 for the normal case and that of the ResNeSt-101 model was 0.676 for the hard case, indicating favorable performances. Overall, the deep learning-based image analysis method is more robust than existing methods, particularly in terms of its ability to accurately identify waste in various forms, such as mixed or piled up waste. [ABSTRACT FROM AUTHOR]